The Experts below are selected from a list of 72720 Experts worldwide ranked by ideXlab platform
Soichiro Ishihara - One of the best experts on this subject based on the ideXlab platform.
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Artificial Intelligence using a convolutional neural network for automatic detection of small bowel angioectasia in capsule endoscopy images
Digestive Endoscopy, 2020Co-Authors: Akiyoshi Tsuboi, Shiro Oka, Kazuharu Aoyama, Hiroaki Saito, Tomonori Aoki, Atsuo Yamada, Tomoki Matsuda, Mitsuhiro Fujishiro, Soichiro Ishihara, Masato NakahoriAbstract:Background and aim Although small-bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule endoscopy (CE) in patients with obscure gastrointestinal bleeding, a computer-aided detection method has not been established. We developed an Artificial Intelligence System with deep learning that can automatically detect small-bowel angioectasia in CE images. Methods We trained a deep convolutional neural network (CNN) System based on Single Shot Multibox Detector using 2237 CE images of angioectasia. We assessed its diagnostic accuracy by calculating the area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity, positive predictive value, and negative predictive value using an independent test set of 10 488 small-bowel images, including 488 images of small-bowel angioectasia. Results The AUC to detect angioectasia was 0.998. Sensitivity, specificity, positive predictive value, and negative predictive value of CNN were 98.8%, 98.4%, 75.4%, and 99.9%, respectively, at a cut-off value of 0.36 for the probability score. Conclusions We developed and validated a new System based on CNN to automatically detect angioectasia in CE images. This may be well applicable to daily clinical practice to reduce the burden of physicians as well as to reduce oversight.
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automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network
Gastrointestinal Endoscopy, 2019Co-Authors: Tomonori Aoki, Akiyoshi Tsuboi, Shiro Oka, Kazuharu Aoyama, Hiroaki Saito, Atsuo Yamada, Mitsuhiro Fujishiro, Ayako Nakada, Ryota Niikura, Soichiro IshiharaAbstract:Background and Aims Although erosions and ulcerations are the most common small-bowel abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection method has not been established. We aimed to develop an Artificial Intelligence System with deep learning to automatically detect erosions and ulcerations in WCE images. Methods We trained a deep convolutional neural network (CNN) System based on a Single Shot Multibox Detector, using 5360 WCE images of erosions and ulcerations. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,440 small-bowel images including 440 images of erosions and ulcerations. Results The trained CNN required 233 seconds to evaluate 10,440 test images. The area under the curve for the detection of erosions and ulcerations was 0.958 (95% confidence interval [CI], 0.947-0.968). The sensitivity, specificity, and accuracy of the CNN were 88.2% (95% CI, 84.8%-91.0%), 90.9% (95% CI, 90.3%-91.4%), and 90.8% (95% CI, 90.2%-91.3%), respectively, at a cut-off value of 0.481 for the probability score. Conclusions We developed and validated a new System based on CNN to automatically detect erosions and ulcerations in WCE images. This may be a crucial step in the development of daily-use diagnostic software for WCE images to help reduce oversights and the burden on physicians.
Abdellah El Moudni - One of the best experts on this subject based on the ideXlab platform.
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forward search algorithm based on dynamic programming for real time adaptive traffic signal control
Iet Intelligent Transport Systems, 2015Co-Authors: Mahjoub Dridi, Abdellah El MoudniAbstract:The scheduling of traffic signal at intersections is involved in an application of Artificial Intelligence System. This study presents a new forward search algorithm based on dynamic programming (FSDP) under a decision tree, and explores an efficient solution for real-time adaptive traffic signal control policy. Traffic signal control with cases of fixed phase sequence and variable phase sequence are both considered in the algorithm. Owing to the properties of forward research dynamic programming and the process optimisation of repeated or invalid traffic states the authors proposed, FSDP algorithm reduces the number of states and saves much computation time. Consequently, FSDP is certain to be an on-line algorithm through its application to a complicated traffic control problem. Moreover, the labelled position method is firstly proposed in the author's study to search the optimal policy after reaching the goal state. For practical operations, this new algorithm is extended by adding the rolling horizon approach, and some derived methods are compared with the optimal fixed-time control and adaptive control on the evaluation of traffic delay. Experimental results obtained by the simulations of symmetrical and asymmetrical traffic flow scenarios show that the FSDP method can perform quite well with high efficiency and good qualities in traffic control.
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Forward search algorithm based on dynamic programming for real-time adaptive traffic signal control
IET Intelligent Transport Systems, 2015Co-Authors: Biao Yin, Mahjoub Dridi, Abdellah El MoudniAbstract:The scheduling of traffic signal at intersections is involved in an application of Artificial Intelligence System. This study presents a new forward search algorithm based on dynamic programming (FSDP) under a decision tree, and explores an efficient solution for real-time adaptive traffic signal control policy. Traffic signal control with cases of fixed phase sequence and variable phase sequence are both considered in the algorithm. Owing to the properties of forward research dynamic programming and the process optimisation of repeated or invalid traffic states the authors proposed, FSDP algorithm reduces the number of states and saves much computation time. Consequently, FSDP is certain to be an on-line algorithm through its application to a complicated traffic control problem. Moreover, the labelled position method is firstly proposed in the author's study to search the optimal policy after reaching the goal state. For practical operations, this new algorithm is extended by adding the rolling horizon approach, and some derived methods are compared with the optimal fixed-time control and adaptive control on the evaluation of traffic delay. Experimental results obtained by the simulations of symmetrical and asymmetrical traffic flow scenarios show that the FSDP method can perform quite well with high efficiency and good qualities in traffic control.
Akiyoshi Tsuboi - One of the best experts on this subject based on the ideXlab platform.
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Artificial Intelligence using a convolutional neural network for automatic detection of small bowel angioectasia in capsule endoscopy images
Digestive Endoscopy, 2020Co-Authors: Akiyoshi Tsuboi, Shiro Oka, Kazuharu Aoyama, Hiroaki Saito, Tomonori Aoki, Atsuo Yamada, Tomoki Matsuda, Mitsuhiro Fujishiro, Soichiro Ishihara, Masato NakahoriAbstract:Background and aim Although small-bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule endoscopy (CE) in patients with obscure gastrointestinal bleeding, a computer-aided detection method has not been established. We developed an Artificial Intelligence System with deep learning that can automatically detect small-bowel angioectasia in CE images. Methods We trained a deep convolutional neural network (CNN) System based on Single Shot Multibox Detector using 2237 CE images of angioectasia. We assessed its diagnostic accuracy by calculating the area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity, positive predictive value, and negative predictive value using an independent test set of 10 488 small-bowel images, including 488 images of small-bowel angioectasia. Results The AUC to detect angioectasia was 0.998. Sensitivity, specificity, positive predictive value, and negative predictive value of CNN were 98.8%, 98.4%, 75.4%, and 99.9%, respectively, at a cut-off value of 0.36 for the probability score. Conclusions We developed and validated a new System based on CNN to automatically detect angioectasia in CE images. This may be well applicable to daily clinical practice to reduce the burden of physicians as well as to reduce oversight.
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automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network
Gastrointestinal Endoscopy, 2019Co-Authors: Tomonori Aoki, Akiyoshi Tsuboi, Shiro Oka, Kazuharu Aoyama, Hiroaki Saito, Atsuo Yamada, Mitsuhiro Fujishiro, Ayako Nakada, Ryota Niikura, Soichiro IshiharaAbstract:Background and Aims Although erosions and ulcerations are the most common small-bowel abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection method has not been established. We aimed to develop an Artificial Intelligence System with deep learning to automatically detect erosions and ulcerations in WCE images. Methods We trained a deep convolutional neural network (CNN) System based on a Single Shot Multibox Detector, using 5360 WCE images of erosions and ulcerations. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,440 small-bowel images including 440 images of erosions and ulcerations. Results The trained CNN required 233 seconds to evaluate 10,440 test images. The area under the curve for the detection of erosions and ulcerations was 0.958 (95% confidence interval [CI], 0.947-0.968). The sensitivity, specificity, and accuracy of the CNN were 88.2% (95% CI, 84.8%-91.0%), 90.9% (95% CI, 90.3%-91.4%), and 90.8% (95% CI, 90.2%-91.3%), respectively, at a cut-off value of 0.481 for the probability score. Conclusions We developed and validated a new System based on CNN to automatically detect erosions and ulcerations in WCE images. This may be a crucial step in the development of daily-use diagnostic software for WCE images to help reduce oversights and the burden on physicians.
Annet Meijers - One of the best experts on this subject based on the ideXlab platform.
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covid 19 on chest radiographs a multireader evaluation of an Artificial Intelligence System
Radiology, 2020Co-Authors: Keelin Murphy, Henk Smits, Arnoud J G Knoops, Michael B J M Korst, Tijs Samson, Ernst T Scholten, Steven Schalekamp, Cornelia M Schaeferprokop, Rick H H M Philipsen, Annet MeijersAbstract:An Artificial Intelligence System (CAD4COVID-XRay) can identify characteristics of coronavirus disease 2019 on chest radiographs with performance comparable to that of six readers.
Tomonori Aoki - One of the best experts on this subject based on the ideXlab platform.
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Artificial Intelligence using a convolutional neural network for automatic detection of small bowel angioectasia in capsule endoscopy images
Digestive Endoscopy, 2020Co-Authors: Akiyoshi Tsuboi, Shiro Oka, Kazuharu Aoyama, Hiroaki Saito, Tomonori Aoki, Atsuo Yamada, Tomoki Matsuda, Mitsuhiro Fujishiro, Soichiro Ishihara, Masato NakahoriAbstract:Background and aim Although small-bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule endoscopy (CE) in patients with obscure gastrointestinal bleeding, a computer-aided detection method has not been established. We developed an Artificial Intelligence System with deep learning that can automatically detect small-bowel angioectasia in CE images. Methods We trained a deep convolutional neural network (CNN) System based on Single Shot Multibox Detector using 2237 CE images of angioectasia. We assessed its diagnostic accuracy by calculating the area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity, positive predictive value, and negative predictive value using an independent test set of 10 488 small-bowel images, including 488 images of small-bowel angioectasia. Results The AUC to detect angioectasia was 0.998. Sensitivity, specificity, positive predictive value, and negative predictive value of CNN were 98.8%, 98.4%, 75.4%, and 99.9%, respectively, at a cut-off value of 0.36 for the probability score. Conclusions We developed and validated a new System based on CNN to automatically detect angioectasia in CE images. This may be well applicable to daily clinical practice to reduce the burden of physicians as well as to reduce oversight.
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automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network
Gastrointestinal Endoscopy, 2019Co-Authors: Tomonori Aoki, Akiyoshi Tsuboi, Shiro Oka, Kazuharu Aoyama, Hiroaki Saito, Atsuo Yamada, Mitsuhiro Fujishiro, Ayako Nakada, Ryota Niikura, Soichiro IshiharaAbstract:Background and Aims Although erosions and ulcerations are the most common small-bowel abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection method has not been established. We aimed to develop an Artificial Intelligence System with deep learning to automatically detect erosions and ulcerations in WCE images. Methods We trained a deep convolutional neural network (CNN) System based on a Single Shot Multibox Detector, using 5360 WCE images of erosions and ulcerations. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,440 small-bowel images including 440 images of erosions and ulcerations. Results The trained CNN required 233 seconds to evaluate 10,440 test images. The area under the curve for the detection of erosions and ulcerations was 0.958 (95% confidence interval [CI], 0.947-0.968). The sensitivity, specificity, and accuracy of the CNN were 88.2% (95% CI, 84.8%-91.0%), 90.9% (95% CI, 90.3%-91.4%), and 90.8% (95% CI, 90.2%-91.3%), respectively, at a cut-off value of 0.481 for the probability score. Conclusions We developed and validated a new System based on CNN to automatically detect erosions and ulcerations in WCE images. This may be a crucial step in the development of daily-use diagnostic software for WCE images to help reduce oversights and the burden on physicians.
